import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				{}

				__lowercase					     =				job['''started_at''']
				__lowercase					     =				job['''completed_at''']

				__lowercase					     =				date_parser.parse(lowercase				)
				__lowercase					     =				date_parser.parse(lowercase				)

				__lowercase					     =				round((end_datetime - start_datetime).total_seconds() / 60.0				)

				__lowercase					     =				start
				__lowercase					     =				end
				__lowercase					     =				duration_in_min

				return job_info





def 							UpperCAmelCase		(       lowercase				,     lowercase=None				):







				"""simple docstring"""



				__lowercase					     =				None
				if token is not None:
								__lowercase					     =				{'''Accept''': '''application/vnd.github+json''', '''Authorization''': F"Bearer {token}"}

				__lowercase					     =				F"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
				__lowercase					     =				requests.get(lowercase				,     headers=lowercase				).json()
				__lowercase					     =				{}

				try:
								job_time.update({job['''name''']: extract_time_from_single_job(lowercase				) for job in result['''jobs''']}				)
								__lowercase					     =				math.ceil((result['''total_count'''] - 100) / 100				)

								for i in range(lowercase				):
												__lowercase					     =				requests.get(url + F"&page={i + 2}"				,     headers=lowercase				).json()
												job_time.update({job['''name''']: extract_time_from_single_job(lowercase				) for job in result['''jobs''']}				)

								return job_time
				except Exception:
								print(F"Unknown error, could not fetch links:\n{traceback.format_exc()}"				)

				return {}


if __name__ == "__main__":
		__a    :	int         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
		__a    :	str         =    parser.parse_args()

		__a    :	List[str]         =    get_job_time(args.workflow_run_id)
		__a    :	Tuple         =    dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

		for k, v in job_time.items():
				print(F'''{k}: {v["duration"]}''')
import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
__a    :	Tuple         =    logging.get_logger(__name__)





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				SwinConfig.from_pretrained(
				    '''microsoft/swin-tiny-patch4-window7-224'''				,     out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				)
				__lowercase					     =				MaskFormerConfig(backbone_config=lowercase				)

				__lowercase					     =				'''huggingface/label-files'''
				if "ade20k-full" in model_name:
								# this should be ok
								__lowercase					     =				847
								__lowercase					     =				'''maskformer-ade20k-full-id2label.json'''
				elif "ade" in model_name:
								# this should be ok
								__lowercase					     =				150
								__lowercase					     =				'''ade20k-id2label.json'''
				elif "coco-stuff" in model_name:
								# this should be ok
								__lowercase					     =				171
								__lowercase					     =				'''maskformer-coco-stuff-id2label.json'''
				elif "coco" in model_name:
								# TODO
								__lowercase					     =				133
								__lowercase					     =				'''coco-panoptic-id2label.json'''
				elif "cityscapes" in model_name:
								# this should be ok
								__lowercase					     =				19
								__lowercase					     =				'''cityscapes-id2label.json'''
				elif "vistas" in model_name:
								# this should be ok
								__lowercase					     =				65
								__lowercase					     =				'''mapillary-vistas-id2label.json'''

				__lowercase					     =				json.load(open(hf_hub_download(lowercase				,     lowercase				,     repo_type='''dataset'''				)				,     '''r'''				)				)
				__lowercase					     =				{int(lowercase				): v for k, v in idalabel.items()}

				return config





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				[]
				# stem
				# fmt: off
				rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')				)
				rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')				)
				rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')				)
				rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''')				)
				# stages
				for i in range(len(config.backbone_config.depths				)				):
								for j in range(config.backbone_config.depths[i]				):
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm1.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")				)
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm1.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")				)
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")				)
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.relative_position_index", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")				)
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.proj.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")				)
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.proj.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")				)
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm2.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")				)
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm2.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")				)
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc1.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")				)
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc1.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")				)
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc2.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")				)
												rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc2.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")				)

								if i < 3:
												rename_keys.append((F"backbone.layers.{i}.downsample.reduction.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight")				)
												rename_keys.append((F"backbone.layers.{i}.downsample.norm.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight")				)
												rename_keys.append((F"backbone.layers.{i}.downsample.norm.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias")				)
								rename_keys.append((F"backbone.norm{i}.weight", F"model.pixel_level_module.encoder.hidden_states_norms.{i}.weight")				)
								rename_keys.append((F"backbone.norm{i}.bias", F"model.pixel_level_module.encoder.hidden_states_norms.{i}.bias")				)

				# FPN
				rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')				)
				rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')				)
				rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')				)
				for source_index, target_index in zip(range(3				,     0				,     -1				)				,     range(0				,     3				)				):
								rename_keys.append((F"sem_seg_head.adapter_{source_index}.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight")				)
								rename_keys.append((F"sem_seg_head.adapter_{source_index}.norm.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight")				)
								rename_keys.append((F"sem_seg_head.adapter_{source_index}.norm.bias", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias")				)
								rename_keys.append((F"sem_seg_head.layer_{source_index}.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight")				)
								rename_keys.append((F"sem_seg_head.layer_{source_index}.norm.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight")				)
								rename_keys.append((F"sem_seg_head.layer_{source_index}.norm.bias", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias")				)
				rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')				)
				rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')				)

				# Transformer decoder
				for idx in range(config.decoder_config.decoder_layers				):
								# self-attention out projection
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight", F"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight")				)
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias", F"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias")				)
								# cross-attention out projection
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight", F"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight")				)
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias", F"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias")				)
								# MLP 1
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight", F"model.transformer_module.decoder.layers.{idx}.fc1.weight")				)
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias", F"model.transformer_module.decoder.layers.{idx}.fc1.bias")				)
								# MLP 2
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight", F"model.transformer_module.decoder.layers.{idx}.fc2.weight")				)
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias", F"model.transformer_module.decoder.layers.{idx}.fc2.bias")				)
								# layernorm 1 (self-attention layernorm)
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight", F"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight")				)
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias", F"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias")				)
								# layernorm 2 (cross-attention layernorm)
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight", F"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight")				)
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias", F"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias")				)
								# layernorm 3 (final layernorm)
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight", F"model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight")				)
								rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias", F"model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias")				)

				rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')				)
				rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')				)

				# heads on top
				rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')				)

				rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')				)
				rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')				)

				rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')				)
				rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')				)

				for i in range(3				):
								rename_keys.append((F"sem_seg_head.predictor.mask_embed.layers.{i}.weight", F"mask_embedder.{i}.0.weight")				)
								rename_keys.append((F"sem_seg_head.predictor.mask_embed.layers.{i}.bias", F"mask_embedder.{i}.0.bias")				)
				# fmt: on

				return rename_keys





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				dct.pop(lowercase				)
				__lowercase					     =				val





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				[int(backbone_config.embed_dim * 2**i				) for i in range(len(backbone_config.depths				)				)]
				for i in range(len(backbone_config.depths				)				):
								__lowercase					     =				num_features[i]
								for j in range(backbone_config.depths[i]				):
												# fmt: off
												# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
												__lowercase					     =				state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.weight"				)
												__lowercase					     =				state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.bias"				)
												# next, add query, keys and values (in that order) to the state dict
												__lowercase					     =				in_proj_weight[:dim, :]
												__lowercase					     =				in_proj_bias[: dim]
												__lowercase					     =				in_proj_weight[
												    dim : dim * 2, :
												]
												__lowercase					     =				in_proj_bias[
												    dim : dim * 2
												]
												__lowercase					     =				in_proj_weight[
												    -dim :, :
												]
												__lowercase					     =				in_proj_bias[-dim :]
												# fmt: on





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				config.decoder_config.hidden_size
				for idx in range(config.decoder_config.decoder_layers				):
								# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
								__lowercase					     =				state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"				)
								__lowercase					     =				state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"				)
								# next, add query, keys and values (in that order) to the state dict
								__lowercase					     =				in_proj_weight[: hidden_size, :]
								__lowercase					     =				in_proj_bias[:config.hidden_size]
								__lowercase					     =				in_proj_weight[hidden_size : hidden_size * 2, :]
								__lowercase					     =				in_proj_bias[hidden_size : hidden_size * 2]
								__lowercase					     =				in_proj_weight[-hidden_size :, :]
								__lowercase					     =				in_proj_bias[-hidden_size :]
								# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
								__lowercase					     =				state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"				)
								__lowercase					     =				state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"				)
								# next, add query, keys and values (in that order) to the state dict
								__lowercase					     =				in_proj_weight[: hidden_size, :]
								__lowercase					     =				in_proj_bias[:config.hidden_size]
								__lowercase					     =				in_proj_weight[hidden_size : hidden_size * 2, :]
								__lowercase					     =				in_proj_bias[hidden_size : hidden_size * 2]
								__lowercase					     =				in_proj_weight[-hidden_size :, :]
								__lowercase					     =				in_proj_bias[-hidden_size :]
				# fmt: on





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__lowercase					     =				Image.open(requests.get(lowercase				,     stream=lowercase				).raw				)
				return im





@torch.no_grad()
def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				,     lowercase = False				):







				"""simple docstring"""



				__lowercase					     =				get_maskformer_config(lowercase				)

				# load original state_dict
				with open(lowercase				,     '''rb'''				) as f:
								__lowercase					     =				pickle.load(lowercase				)
				__lowercase					     =				data['''model''']

				# for name, param in state_dict.items():
				#     print(name, param.shape)

				# rename keys
				__lowercase					     =				create_rename_keys(lowercase				)
				for src, dest in rename_keys:
								rename_key(lowercase				,     lowercase				,     lowercase				)
				read_in_swin_q_k_v(lowercase				,     config.backbone_config				)
				read_in_decoder_q_k_v(lowercase				,     lowercase				)

				# update to torch tensors
				for key, value in state_dict.items():
								__lowercase					     =				torch.from_numpy(lowercase				)

				# load 🤗 model
				__lowercase					     =				MaskFormerForInstanceSegmentation(lowercase				)
				model.eval()

				for name, param in model.named_parameters():
								print(lowercase				,     param.shape				)

				__lowercase     ,	__lowercase					     =				model.load_state_dict(lowercase				,     strict=lowercase				)
				assert missing_keys == [
				    "model.pixel_level_module.encoder.model.layernorm.weight",
				    "model.pixel_level_module.encoder.model.layernorm.bias",
				]
				assert len(lowercase				) == 0, F"Unexpected keys: {unexpected_keys}"

				# verify results
				__lowercase					     =				prepare_img()
				if "vistas" in model_name:
								__lowercase					     =				65
				elif "cityscapes" in model_name:
								__lowercase					     =				65535
				else:
								__lowercase					     =				255
				__lowercase					     =				True if '''ade''' in model_name else False
				__lowercase					     =				MaskFormerImageProcessor(ignore_index=lowercase				,     reduce_labels=lowercase				)

				__lowercase					     =				image_processor(lowercase				,     return_tensors='''pt'''				)

				__lowercase					     =				model(**lowercase				)

				print('''Logits:'''				,     outputs.class_queries_logits[0, :3, :3]				)

				if model_name == "maskformer-swin-tiny-ade":
								__lowercase					     =				torch.tensor(
								    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]				)
				assert torch.allclose(outputs.class_queries_logits[0, :3, :3]				,     lowercase				,     atol=1E-4				)
				print('''Looks ok!'''				)

				if pytorch_dump_folder_path is not None:
								print(F"Saving model and image processor to {pytorch_dump_folder_path}"				)
								Path(lowercase				).mkdir(exist_ok=lowercase				)
								model.save_pretrained(lowercase				)
								image_processor.save_pretrained(lowercase				)

				if push_to_hub:
								print('''Pushing model and image processor to the hub...'''				)
								model.push_to_hub(F"nielsr/{model_name}"				)
								image_processor.push_to_hub(F"nielsr/{model_name}"				)


if __name__ == "__main__":
		__a    :	Union[str, Any]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--model_name""",
		    default="""maskformer-swin-tiny-ade""",
		    type=str,
		    help=("""Name of the MaskFormer model you'd like to convert""",),
		)
		parser.add_argument(
		    """--checkpoint_path""",
		    default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
		    type=str,
		    help="""Path to the original state dict (.pth file).""",
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
		)
		parser.add_argument(
		    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
		)

		__a    :	str         =    parser.parse_args()
		convert_maskformer_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)